A Study On The Detection Of Exudate Lesions In Retinal Fundus Images

Year 2020, , 27 - 36, 31.01.2020

Ümit Atila , Kemal Akyol , Furkan Sabaz

https://doi.org/10.17671/gazibtd.550022

Cited By: 2

Abstract

Diabetes Mellitus, known also as diabetes affects the structure of the eye, impairs the working of it, and then causes loss of vision. Based on the excessive increase of glucose level in diabetic retinopathy patients, various lesions form in their retinas. Automatic detection of these lesions is relatively important for early detection of the disease. In this study, a method is suggested, which automatically detects the exudates in the retinas of diabetic retinopathy patients. This method consists of feature extraction using SURF keypoint algorithm, and detection of lesions using Support Vector Machine, Multi-layer Perceptron and Random Forest algorithms. The performance of proposed method was evaluated on DIARETDB0 and DIARETDB1 databases. For both databases, RF algorithm, which provides 95,8% and 92,4% accuracies respectively, was the most successful algorithm. Detected exudate lesions using this algorithm were labeled as hard and soft exudates based on ground truth information of considered databases.

Keywords

diabetic retinopathy, exudate, hard exudate, soft exudate, jaccard index, machine learning

Retinal Görüntülerde Eksuda Lezyonlarının Tespiti Üzerine Bir Çalışma

Abstract

Göz yapısını etkileyen bir hastalık olarak bilinen şeker hastalığı gözün çalışmasını etkiler ve görme kaybına neden olur. Diyabetik retinopati hastalarının şeker seviyesindeki aşırı artışa bağlı olarak bu hastaların retinalarında çeşitli lezyonlar oluşur. Bu lezyonların otomatik tespiti bu hastalığın erken teşhisi için önemli bir unsurdur. Bu çalışmada, diyabetik retinopati hastalarının retinalarındaki eksuda lezyonlarını otomatik olarak tespit eden bir metot önerilmektedir. Bu metot SURF anahtar nokta algoritması ile özellik çıkartımı ve sonrasında Destek Vektör Makineleri, Çok Katmanlı Algılayıcı ve Rasgele Orman algoritmalarıyla lezyonların tespitini içermektedir. Önerilen yöntemin performansı DIARETDB0 ve DIARETDB1 veri tabanları üzerinde gözlemlenmiştir. Her iki veri tabanı içinde sırasıyla %95,8 ve %92,4 doğrulukla Rasgele Orman algoritması en başarılı sonuçları vermiştir. Bu algoritma ile tespit edilen eksuda lezyonları, ilgili veri tabanlarının kesin referans bilgilerine dayanılarak sert ve yumuşak eksuda olarak etiketlenmiştir.

Keywords

diyabetik retinopati, eksuda, sert eksuda, soft eksuda, jaccard indeksi, makine öğrenmesi

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